Achim Röder

Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands

The development of vegetation cover is one of the primary indicators for land degradation, stability, or regeneration in regions threatened by overgrazing. This paper addresses the problem how spatially explicit information about degradation processes in European Mediterranean rangelands can be derived from long time series of satellite data. The selected test site in central Crete, Greece, is considered to be representative for the highly heterogeneous character of such landscapes. The monitoring approach comprises the time period between 1977 and 1996, covered by nine Landsat TM and four Landsat MSS images. Special emphasis has hence been put on the evaluation of potentials and drawbacks when coupling Landsat TM and MSS based results. The data sets were geometrically and radiometrically pre-processed in a rigorous fashion, followed by a linear spectral unmixing approach and a time series analysis of vegetation fraction images. Based on the resulting map, the spatio-temporal patterns of vegetation cover changes are explained. Even a test site such as central Crete, with its limited spatial extend, exhibits heterogeneous patterns of change, supporting the hypothesis that long time series of EOS data from Landsat-like sensors are mandatory to identify the relevant changes at landscape level.

Modelling the effects of landscape fuel treatments on fire growth and behaviour in a Mediterranean landscape (eastern Spain)

The number of large fires increased in the 1970s in the Valencia region (eastern Spain), as in most northern Mediterranean countries, owing to the fuel accumulation that affected large areas as a consequence of an intensive land abandonment. The Ayora site (Valencia province) was affected by a large fire in July 1979. We parameterised the fire growth model FARSITE for the 1979 fire conditions using remote sensing-derived fuel cartography. We simulated different fuel scenarios to study the interactions between fuel spatial distribution and fire characteristics (area burned, rate of spread and fireline intensity). We then tested the effectiveness of several firebreak networks on fire spread control. Simulations showed that fire propagation and behaviour were greatly influenced by fuel spatial distribution. The fragmentation of large dense shrubland areas through the introduction of wooded patches strongly reduced fire size, generally slowing fire and limiting fireline intensity. Both the introduction of forest corridors connecting woodlands and the promotion of complex shapes for wooded patches decreased the area burned. Firebreak networks were always very effective in reducing fire size and their effect was enhanced in appropriate fuel-altered scenarios. Most firebreak alternatives, however, did not reduce either rate of fire spread or fireline intensity.

Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna

The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegetation patterns and dynamics at regional scale; however, the low temporal frequency is often a limitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflectance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) and Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatiotemporal resolution dataset. A time series of 333 STARFM images was generated between February 2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a 12 × 10     km heterogeneous test area within the North Queensland Savannas. Time series of observed Landsat and predicted STARFM images correlated high for each spectral band (0.89 to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change events were analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relationship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVI data (root mean square error of 0.027). A phenological description of the major vegetation classes within the region revealed distinct differences and lag times within the ecosystem. The 2004 dry season NDVI minimum-map correlated highly with the validated 2004 foliage projective cover product ( r 2 = 0.92 ) from the Queensland Department of Environment and Resource Management.

Retrospective studies of grazing-induced land degradation: A case study in central Crete, Greece

Although it is well known that livestock husbandry has become more prevalent in Crete, Greece, during the last two decades, it is not well understood how grazing pressure differs spatially and how it influences grazed rangelands over time. To enhance the understanding of spatio-temporal degradation processes, a 20-year time series of Landsat Thematic Mapper and Multi-Spectral Scanner data is analysed. A set of vegetation fraction images is derived, based on a rigorous pre-processing scheme and linear spectral unmixing. Patterns of vegetation degradation, stability and regeneration are subsequently mapped by means of a trend analysis. It can be concluded that about 40% of the central Cretan rangelands show a declining trend in vegetation cover between 1977 and 1996. A deeper understanding of the underlying driving forces can be achieved through integrating remote sensing-derived information with other geoinformation sources. A comparison between local stress factors and vegetation development reveals that degradation processes are most evident when boundary conditions are favourable. It is, hence, assumed that increased grazing pressure is a major driving force for decreasing vegetation cover in the long term.

Separating grassland and shrub vegetation by multidate pixel‐adaptive spectral mixture analysis

Monitoring and assessment of land degradation and the processes driving it require effective change indicators at appropriate scales and spatial extent. In this context, the decomposition of Mediterranean rangeland vegetation into woody and herbaceous fractions is of great significance. This study demonstrates that a stratification of vegetation into woody and herbaceous components is possible with two satellite images of moderate spatial and spectral resolution. We used a pixel‐adaptive spectral mixture analysis to derive subpixel‐level vegetation abundances from satellite imagery representing two specific phenological stages of Mediterranean rangeland vegetation. The transferability of endmember models is often a problem of multidate spectral mixture analysis because of uneven spectral dimensionality within and among datasets. In our approach, the dimensionality of the mixture model was determined automatically, based on error calculations. This method enables the transfer of the mixture model to multiple scenes and allows for quantitative comparison of vegetation abundances. The results show that the woody vegetation fraction corresponds well with field data (R 2 = 0.76–0.91) and vegetation cover mapped from a very high resolution satellite image. The herbaceous vegetation fraction displays a good correlation compared to field mapped cover but still implies a moderate level of uncertainty (R 2 = 0.52–0.76). The approach pursued in this research may be valuable for the characterization of rangeland plant communities and for the derivation of vegetation‐related indicators useful for the monitoring and assessment of degradation.

An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications

We developed a large-area preprocessing framework for multisensor Landsat data, capable of processing large data volumes. Cloud and cloud shadow detection is performed by a modified Fmask code. Surface reflectance is inferred from Tanrés formulation of the radiative transfer, including adjacency effect correction. A precompiled MODIS water vapor database provides daily or climatological fallback estimates. Aerosol optical depth (AOD) is estimated over dark objects (DOs) that are identified in a combined database and image-based approach, where information on their temporal persistency is utilized. AOD is inferred with consideration of the actual target reflectance and background contamination effect. In case of absent DOs in bright scenes, a fallback approach with a modeled AOD climatology is used instead. Topographic normalization is performed by a modified C-correction. The data are projected into a single coordinate system and are organized in a gridded data structure for simplified pixel-based access. We based the assessment of the produced data set on an exhaustive analysis of overlapping pixels: 98.8% of the redundant overlaps are in the range of the expected ±2.5% overall radiometric algorithm accuracy. AOD is in very good agreement with Aerosol Robotic Network sunphotometer data (R2: 0.72 to 0.79, low intercepts, and slopes near unity). The uncertainty in using the water vapor fallback climatology is approximately ±2.8% for the TM SWIR1 band in the wet season. The topographic correction was considered successful by an investigation of the nonrelationship between the illumination angle and the corrected radiance.

Evaluating the trade-off between food and timber resulting from the conversion of Miombo forests to agricultural land in Angola using multi-temporal Landsat data.

The repopulation of abandoned areas in Angola after 27years of civil war led to a fast and extensive expansion of agricultural fields to meet the rising food demand. Yet, the increase in crop production at the expense of natural resources carries an inherent potential for conflicts since the demand for timber and wood extraction are also supposed to rise. We use the concept of ecosystem services to evaluate the trade-off between food and woody biomass. Our study area is located in central Angola, in the highlands of the upper Okavango catchment. We used Landsat data (spatial resolution: 30×30m) with a bi-temporal and multi-seasonal change detection approach for five time steps between 1989 and 2013 to estimate the conversion area from woodland to agriculture. Overall accuracy is 95%, users accuracy varies from 89-95% and producers accuracy ranges between 92-99%. To quantify the trade-off between woody biomass and the amount of food, this information was combined with indicator values and we furthermore assessed biomass regrowth on fallows. Our results reveal a constant rise in agricultural expansion from 1989-2013 with the mean annual deforestation rate increasing from roughly 5300ha up to about 12,000ha. Overall, 5.6% of the forested areas were converted to agriculture, whereas the FAO states a national deforestation rate for Angola of 5% from 1990-2010 (FAO, 2010). In the last time step 961,000t per year of woodland were cleared to potentially produce 1240t per year of maize. Current global agro-economical projections forecast increasing pressure on tropical dry forests from large-scale agriculture schemes (Gasparri et al., 2015; Searchinger and Heimlich, 2015). Our study underlines the importance of considering subsistence-related change processes, which may contribute significantly to negative effects associated with deforestation and degradation of these forest ecosystems.

Functional Habitat Heterogeneity and Large Herbivore Seasonal Habitat Selection in Northern Botswana

This study aimed to determine the functional seasonal attributes for herbivores of the major habitats and landscapes of the Savuti-Mababe-Linyanti ecosystem (SMLE) of northern Botswana and how various herbivore species responded to this heterogeneity. Floodplain grasslands and dambo grasslands provided the only significant green forage and biomass during the late dry season, whereas short grasslands of the Mababe Depression provided the highest forage quality of all habitats during the wet season. The ability to provide reliable forage and drinking water in floodplain,swampand dambo grasslands attracted large concentrations of zebra and buffalo during the dry season, which was mediated by fire. Large concentrations of zebra were observed in mineral-rich grasslands of the Mababe Depression during the wet season, whereas buffalo were not observed in these open grassland landscapes in this season. Other herbivore species appeared to use the same landscapes year-round where lechwe were observed mainly in floodplain and swamp landscapes on the western edge of the Linyanti Swamps, while wildebeest and impala were observed in floodplains and adjacent woodlands near permanent water suggesting that these species had access to sufficient resources at a landscape scale. Thus various herbivore species responded differently to functional heterogeneity across seasons and scales.

Using Annual Landsat Time Series for the Detection of Dry Forest Degradation Processes in South-Central Angola

Dry tropical forests undergo massive conversion and degradation processes. This also holds true for the extensive Miombo forests that cover large parts of Southern Africa. While the largest proportional area can be found in Angola, the country still struggles with food shortages, insufficient medical and educational supplies, as well as the ongoing reconstruction of infrastructure after 27 years of civil war. Especially in rural areas, the local population is therefore still heavily dependent on the consumption of natural resources, as well as subsistence agriculture. This leads, on one hand, to large areas of Miombo forests being converted for cultivation purposes, but on the other hand, to degradation processes due to the selective use of forest resources. While forest conversion in south-central rural Angola has already been quantitatively described, information about forest degradation is not yet available. This is due to the history of conflicts and the therewith connected research difficulties, as well as the remote location of this area. We apply an annual time series approach using Landsat data in south-central Angola not only to assess the current degradation status of the Miombo forests, but also to derive past developments reaching back to times of armed conflicts. We use the Disturbance Index based on tasseled cap transformation to exclude external influences like inter-annual variation of rainfall. Based on this time series, linear regression is calculated for forest areas unaffected by conversion, but also for the pre-conversion period of those areas that were used for cultivation purposes during the observation time. Metrics derived from linear regression are used to classify the study area according to their dominant modification processes. We compare our results to MODIS latent integral trends and to further products to derive information on underlying drivers. Around 13% of the Miombo forests are affected by degradation processes, especially along streets, in villages, and close to existing agriculture. However, areas in presumably remote and dense forest areas are also affected to a significant extent. A comparison with MODIS derived fire ignition data shows that they are most likely affected by recurring fires and less by selective timber extraction. We confirm that areas that are used for agriculture are more heavily disturbed by selective use beforehand than those that remain unaffected by conversion. The results can be substantiated by the MODIS latent integral trends and we also show that due to extent and location, the assessment of forest conversion is most likely not sufficient to provide good estimates for the loss of natural resources.

Improving the Spatial Resolution of Land Surface Phenology by Fusing Medium- and Coarse-Resolution Inputs

Satellite-derived land surface phenology (LSP) serves as a valuable input source for many environmental applications such as land cover classifications and global change studies. Commonly, LSP is derived from coarse-resolution (CR) sensors due to their well-suited temporal resolution. However, LSP is increasingly demanded at medium resolution (MR), but inferring LSP directly from MR imagery remains a challenging task (e.g., due to acquisition frequency). As such, we present a methodology that directly predicts MR LSP on the basis of the respective CR LSP and MR reflectance imagery. The approach considers information from the local pixel neighborhood at both resolutions by utilizing several prediction proxies, including spectral distance and multiscale heterogeneity metrics. The prediction performs well with simulated data